A Fast and Reliable Approach to TSP using Positively Self-feedbacked Hopfield Networks
スポンサーリンク
概要
- 論文の詳細を見る
Abstract in this paper, a fast and reliable approach to the Traveling Salesman Problem (TSP) using the positively self-feedbacked Hopfield networks is proposed. The Hopfield networks with positive self-feedbacks and its collective computational properties are studied. It is proved theoretically and confirmed by simulating the randomly generated Hopfield network with positive self-feedbacks that the emergent collective properties of the original Hopfield network also are present in this network. The network is applied to the TSP and results of computer simulations are presented and used to illustrate the computation power of the networks. The simulation results show that the Hopfield networks with positive self-feedbacks has a rate of success higher than the original Hopfield network for solving the TSP, and converges faster to stable solution than the original Hopfield network does.
- 社団法人 電気学会の論文
- 2004-11-01
著者
-
TANG Zheng
Faculty of Engineering, Toyama University
-
Wang Rong
The Faculty Of Engineering Fukui University
-
Wang Rong
Faculty Of Engineering Toyama University
-
Tang Zheng
Faculty Of Engineering Miyazaki University
-
Wang Rong
Faculty Of Engineering Fukui University
-
Li Y
Southeast Univ.
-
LI Yong
Faculty of Engineering, Toyama University
-
XIA Guangpu
Faculty of Engineering, Toyama University
-
XU Xinshun
Faculty of Engineering, Toyama University
-
Wang Ronglong
Faculty Of Engineering Fukui University
-
Xu Xinshun
Faculty Of Engineering Toyama University
-
Xia Guangpu
Faculty Of Engineering Toyama University
-
Li Yong
Faculty Of Engineering Toyama University
関連論文
- Multilayer Network Learning Algorithm Based on Pattern Search Method(Neural Networks and Bioengineering)
- A Local Search Based Learning Method for Multiple-Valued Logic Networks(Neural Networks and Bioengineering)
- A Method of Learning for Multi-Layer Networks
- A Parallel Graph Planarization Algorithm Using Gradient Ascent Learning of Hopfield Network
- A Saturation Computation Method of Artificial Binary Neural Networks for Combinatorial Optimization Problems
- A Fast and Reliable Approach to TSP using Positively Self-feedbacked Hopfield Networks
- Objective Function Adjustment Algorithm for Combinatorial Optimization Problems(Numerical Analysis and Optimization)
- An Expanded Maximum Neural Network with Chaotic Dynamics for Cellular Radio Channel Assignment Problem(Nonlinear Problems)
- An Improved Artificial Immune Network Model(Neural Networks and Bioengineering)
- A Neural-based Algorithm for Topological Via-minimization Problem
- A New Method to Solve the Constraint Satisfaction Problem Using the Hopfield Neural Network
- An Artificial Immune Network with Multi-layered B Cells Architecture
- An Artificial Immune System Architecture and Its Applications(Neural Networks and Bioengineering)
- The Fuzzy Immune Network and Its Application to Pattern Recognition(Special Section on Papers Selected from ITC-CSCC 2002)
- Design and realization of a network security model
- Affinity Based Lateral Interaction Artificial Immune System(Human-computer Interaction)
- Avoiding the Local Minima Problem in Backpropagation Algorithm with Modified Error Function(Neural Networks and Bioengineering)
- Antioxidative Activity of (-)-Epigallocatechin-3-(3"-O-methyl) gallate Isolated from Fresh Tea Leaf and Preliminary Results on Its Biological Activity
- An Engineering Immune Network Model for Pattern Recognition
- Pattern Classification Using A Fuzzy Immune Network Model
- D-2-6 A Parallel Direct Search Learning Algorithm for Feed-Forward Neural Networks
- An Improved Maximum Neural Network with Stochastic Dynamics Characteristic for Maximum Clique Problem
- A Near-Optimum Parallel Algorithm for a Graph Layout Problem(Neural Networks and Bioengineering)
- Learning Method of Hopfield Neural Network and Its Application to Traveling Salesman Problem (特集:論文誌C発刊30周年記念)
- A Multiple-Valued Immune Network and Its Applications
- Neuron-MOS Current Mirror Circuit and Its Application to Multi-Valued Logic (Special Issue on Multiple-Valued Logic and Its Applications)
- A 1-V, 1-V_ Input Range, Four-Quadrant Analog Multiplier Using Neuron-MOS Transistors
- Ultra-Low Power Two-MOS Virtual-Short Circuit and Its Application
- 自己学習ファジ-コントロ-ラ
- Design and Implementation of a Calibrating T-Model Neural-Based A/D Converter
- Hopfield Neural Network Learning Using Direct Gradient Descent of Energy Function
- Implementation of T-Model Neural-Based PCM Encoders Using MOS Charge-Mode Circuits
- A Learning Fuzzy Network and Its Applications to Inverted Pendulum System
- An Elastic Net Learning Algorithm for Edge Linking of Images
- Solving Maximum Cut Problem Using Improved Hop field Neural Network
- A Near-Optimum Parallel Algorithm for Bipartite Subgraph Problem Using the Hopfield Neural Network Learning
- Quantum Interference Crossover-Based Clonal Selection Algorithm and Its Application to Traveling Salesman Problem
- An Efficient Neural Algorithm for Two-layer Planarization Problem in Graph Drawing
- Maximum Neural Network with Nonlinear Self-Feedback and Its Application to Maximum Independent Set Problem
- An Expanded Lateral Interactive Clonal Selection Algorithm and Its Application
- Improved Clonal Selection Algorithm Combined with Ant Colony Optimization
- An Improved Clonal Selection Algorithm and Its Application to Traveling Salesman Problems(Neural Networks and Bioengineering)
- A Novel Clonal Selection Algorithm and Its Application to Traveling Salesman Problem(Neural Networks and Bioengineering)
- A stochastic dynamic local search method for learning Multiple-Valued Logic networks
- An Improved Artificial Immune System (AIS) by Considering Different Affinities among Th Cells and Antigens
- Multiple-Valued Static Random-Access-Memory Design and Application : Special Issue on Multiple-Valued integrated Circuits
- An Efficient Algorithm for Minimum Vertex Cover Problem
- Two-Phase Pattern Search-based Learning Method for Multi-layer Neural Network
- A Chaotic Maximum Neural Network for Maximum Clique Problem(Biocybernetics, Neurocomputing)
- A New Parallel Algorithm Analogous to Elastic Net Method for Bipartite Subgraph Problem
- A Parallel Graph Planarization Algorithm Using Gradient Ascent Learning of Hopfield Network
- An Efficient Algorithm for Maximum Clique Problem Using Improved Hopfield Neural Network
- A Saturation Computation Method of Artificial Binary Neural Networks for Combinatorial Optimization Problems
- A Parallel Algorithm for Maximum Cut Problem Using Gradient Ascent Learning of Hopfield Neural Networks
- A Gradient Ascent Learning Algorithm in Weight Domain for Hopfield Neural Networks
- A Hopfield Network Learning Algorithm for Graph Planarization
- A Gradient Ascent Learning Algorithm for Elastic Nets
- A Modified Hopfield Neural Network for the Minimum Vertex Cover Problem
- An Improved Transiently Chaotic Neural Network with Application to the Maximum Clique Problems
- An Elastic Net Learning Algorithm for Edge Linking of Images(Neural Netoworks and Bioengineering)
- A Novel Maximum Neural Network with Stochastic Dynamics for N-Queens Problems
- A Child Verb Learning Model Based on Syntactic Bootstrapping
- Design and Implementations of a Learning T-Model Neural Network
- Investigation and Analysis of Hysteresis in Hopfield and T-Model Neural Networks
- T-Model Neural Network for PCM Encoding
- Solving Facility Layout Problem Using an Improved Genetic Algorithm(Numerical Analysis and Optimization)